
import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_    :			str    =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCamelCase_    :			Any    =	[]
for i in range(6):
							# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
							)
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
							rename_keys.append(
							    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
							)
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
							rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
							# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
							)
							rename_keys.append(
							    (
							        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
							        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
							    )
							)
							rename_keys.append(
							    (
							        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
							        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
							    )
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
							)
							rename_keys.append(
							    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
							)
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
							rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[int]						=    state_dict.pop(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    val







def 							A__       (    lowerCamelCase    )	->						List[Any]:
				UpperCamelCase_:				Optional[Any]						=    OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												UpperCamelCase_:				Optional[Any]						=    key.replace("""backbone.0.body"""  ,     """backbone.conv_encoder.model"""    )
												UpperCamelCase_:				Dict						=    value
								else:
												UpperCamelCase_:				str						=    value

				return new_state_dict







def 							A__       (    lowerCamelCase    )	->						Dict:
				UpperCamelCase_:				List[Any]						=    """"""

				# first: transformer encoder
				for i in range(6    ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								UpperCamelCase_:				Union[str, Any]						=    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''    )
								UpperCamelCase_:				int						=    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''    )
								# next, add query, keys and values (in that order) to the state dict
								UpperCamelCase_:				Tuple						=    in_proj_weight[:2_56, :]
								UpperCamelCase_:				Any						=    in_proj_bias[:2_56]
								UpperCamelCase_:				Tuple						=    in_proj_weight[2_56:5_12, :]
								UpperCamelCase_:				Dict						=    in_proj_bias[2_56:5_12]
								UpperCamelCase_:				List[Any]						=    in_proj_weight[-2_56:, :]
								UpperCamelCase_:				Dict						=    in_proj_bias[-2_56:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6    ):
								# read in weights + bias of input projection layer of self-attention
								UpperCamelCase_:				List[Any]						=    state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''    )
								UpperCamelCase_:				Union[str, Any]						=    state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''    )
								# next, add query, keys and values (in that order) to the state dict
								UpperCamelCase_:				int						=    in_proj_weight[:2_56, :]
								UpperCamelCase_:				List[str]						=    in_proj_bias[:2_56]
								UpperCamelCase_:				List[str]						=    in_proj_weight[2_56:5_12, :]
								UpperCamelCase_:				str						=    in_proj_bias[2_56:5_12]
								UpperCamelCase_:				Tuple						=    in_proj_weight[-2_56:, :]
								UpperCamelCase_:				int						=    in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								UpperCamelCase_:				Union[str, Any]						=    state_dict.pop(
								    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''    )
								UpperCamelCase_:				List[Any]						=    state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''    )
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								UpperCamelCase_:				int						=    in_proj_weight_cross_attn[:2_56, :]
								UpperCamelCase_:				Any						=    in_proj_bias_cross_attn[:2_56]
								UpperCamelCase_:				int						=    in_proj_weight_cross_attn[2_56:5_12, :]
								UpperCamelCase_:				Dict						=    in_proj_bias_cross_attn[2_56:5_12]
								UpperCamelCase_:				List[str]						=    in_proj_weight_cross_attn[-2_56:, :]
								UpperCamelCase_:				Any						=    in_proj_bias_cross_attn[-2_56:]







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_, UpperCamelCase_:				Any						=    image.size
				UpperCamelCase_:				Dict						=    max(lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    8_00 if """detection""" in checkpoint_url else 10_00
				UpperCamelCase_:				Optional[Any]						=    target_max_size / current_max_size
				UpperCamelCase_:				Tuple						=    image.resize((int(round(scale * width    )    ), int(round(scale * height    )    ))    )

				return resized_image







def 							A__       (    lowerCamelCase    )	->						Any:
				UpperCamelCase_:				Optional[Any]						=    F.to_tensor(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    F.normalize(lowerCamelCase  ,     mean=[0.485, 0.456, 0.406]  ,     std=[0.229, 0.224, 0.225]    )
				return image







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				logger.info("""Converting model..."""    )

				# load original state dict
				UpperCamelCase_:				str						=    torch.hub.load_state_dict_from_url(lowerCamelCase  ,     map_location="""cpu"""    )
				# rename keys
				for src, dest in rename_keys:
								rename_key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    rename_backbone_keys(lowerCamelCase    )
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowerCamelCase    )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				UpperCamelCase_:				List[Any]						=    """model."""
				for key in state_dict.copy().keys():
								if not key.startswith("""class_labels_classifier"""    ) and not key.startswith("""bbox_predictor"""    ):
												UpperCamelCase_:				List[Any]						=    state_dict.pop(lowerCamelCase    )
												UpperCamelCase_:				str						=    val
    # create HuggingFace model and load state dict
				UpperCamelCase_:				Dict						=    TableTransformerConfig(
				    backbone="""resnet18"""  ,     mask_loss_coefficient=1  ,     dice_loss_coefficient=1  ,     ce_loss_coefficient=1  ,     bbox_loss_coefficient=5  ,     giou_loss_coefficient=2  ,     eos_coefficient=0.4  ,     class_cost=1  ,     bbox_cost=5  ,     giou_cost=2  ,     )

				if "detection" in checkpoint_url:
								UpperCamelCase_:				Optional[int]						=    15
								UpperCamelCase_:				Optional[Any]						=    2
								UpperCamelCase_:				Union[str, Any]						=    {0: """table""", 1: """table rotated"""}
								UpperCamelCase_:				List[str]						=    idalabel
								UpperCamelCase_:				Optional[Any]						=    {v: k for k, v in idalabel.items()}
				else:
								UpperCamelCase_:				Optional[int]						=    1_25
								UpperCamelCase_:				Any						=    6
								UpperCamelCase_:				Optional[int]						=    {
								    0: """table""",
								    1: """table column""",
								    2: """table row""",
								    3: """table column header""",
								    4: """table projected row header""",
								    5: """table spanning cell""",
								}
								UpperCamelCase_:				List[Any]						=    idalabel
								UpperCamelCase_:				str						=    {v: k for k, v in idalabel.items()}

				UpperCamelCase_:				Any						=    DetrImageProcessor(
				    format="""coco_detection"""  ,     max_size=8_00 if """detection""" in checkpoint_url else 10_00    )
				UpperCamelCase_:				List[str]						=    TableTransformerForObjectDetection(lowerCamelCase    )
				model.load_state_dict(lowerCamelCase    )
				model.eval()

				# verify our conversion
				UpperCamelCase_:				str						=    """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
				UpperCamelCase_:				Any						=    hf_hub_download(repo_id="""nielsr/example-pdf"""  ,     repo_type="""dataset"""  ,     filename=lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    Image.open(lowerCamelCase    ).convert("""RGB"""    )
				UpperCamelCase_:				Dict						=    normalize(resize(lowerCamelCase  ,     lowerCamelCase    )    ).unsqueeze(0    )

				UpperCamelCase_:				Union[str, Any]						=    model(lowerCamelCase    )

				if "detection" in checkpoint_url:
								UpperCamelCase_:				Optional[int]						=    (1, 15, 3)
								UpperCamelCase_:				Optional[Any]						=    torch.tensor(
								    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]    )
								UpperCamelCase_:				str						=    torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]    )

				else:
								UpperCamelCase_:				List[Any]						=    (1, 1_25, 7)
								UpperCamelCase_:				Dict						=    torch.tensor(
								    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]    )
								UpperCamelCase_:				Optional[int]						=    torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]    )

				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, :3, :3]  ,     lowerCamelCase  ,     atol=1E-4    )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,     lowerCamelCase  ,     atol=1E-4    )
				print("""Looks ok!"""    )

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''    )
								Path(lowerCamelCase    ).mkdir(exist_ok=lowerCamelCase    )
								model.save_pretrained(lowerCamelCase    )
								image_processor.save_pretrained(lowerCamelCase    )

				if push_to_hub:
								# Push model to HF hub
								logger.info("""Pushing model to the hub..."""    )
								UpperCamelCase_:				Tuple						=    (
								    """microsoft/table-transformer-detection"""
								    if """detection""" in checkpoint_url
								    else """microsoft/table-transformer-structure-recognition"""
								)
								model.push_to_hub(lowerCamelCase    )
								image_processor.push_to_hub(lowerCamelCase    )


if __name__ == "__main__":
							lowerCamelCase_    :			str    =	argparse.ArgumentParser()

							parser.add_argument(
							    """--checkpoint_url""",
							    default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
							    type=str,
							    choices=[
							        """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
							        """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
							    ],
							    help="""URL of the Table Transformer checkpoint you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)
							lowerCamelCase_    :			Dict    =	parser.parse_args()
							convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			str    =	32







def 							A__       (    lowerCamelCase    )	->						int:
				return int(x / 2**20    )

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __enter__(							self			:							int   ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							UpperCamelCase_:				str						=    torch.cuda.memory_allocated()
							return self




			def __exit__(							self			:							List[Any]    ,						*snake_case_			:							Union[str, Any]   ):
							gc.collect()
							torch.cuda.empty_cache()
							UpperCamelCase_:				List[str]						=    torch.cuda.memory_allocated()
							UpperCamelCase_:				int						=    torch.cuda.max_memory_allocated()
							UpperCamelCase_:				Optional[int]						=    bamb(self.end - self.begin   )
							UpperCamelCase_:				Tuple						=    bamb(self.peak - self.begin   )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"  ,     lowerCamelCase = 3_20  ,     lowerCamelCase = 1_60  ,     )	->						Dict:
				UpperCamelCase_:				str						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    load_dataset(
				    """glue"""  ,     """mrpc"""  ,     split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Optional[Any]						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				UpperCamelCase_:				Dict						=    datasets.map(
				    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     load_from_cache_file=lowerCamelCase    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				int						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase  ,     padding="""max_length"""  ,     max_length=1_28  ,     return_tensors="""pt"""    )
								return tokenizer.pad(lowerCamelCase  ,     padding="""longest"""  ,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				UpperCamelCase_:				List[str]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# Initialize accelerator
				UpperCamelCase_:				Any						=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				str						=    config["""lr"""]
				UpperCamelCase_:				Any						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Tuple						=    int(config["""seed"""]    )
				UpperCamelCase_:				Dict						=    int(config["""batch_size"""]    )
				UpperCamelCase_:				Dict						=    args.model_name_or_path

				set_seed(lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				int						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     args.n_train  ,     args.n_val    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained(lowerCamelCase  ,     return_dict=lowerCamelCase    )

				# Instantiate optimizer
				UpperCamelCase_:				Tuple						=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				UpperCamelCase_:				List[Any]						=    optimizer_cls(params=model.parameters()  ,     lr=lowerCamelCase    )

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Any						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								UpperCamelCase_:				List[Any]						=    1
				UpperCamelCase_:				Optional[int]						=    (len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				Dict						=    DummyScheduler(lowerCamelCase  ,     total_num_steps=lowerCamelCase  ,     warmup_num_steps=0    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# We need to keep track of how many total steps we have iterated over
				UpperCamelCase_:				str						=    0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCamelCase_:				Dict						=    0

				# Now we train the model
				UpperCamelCase_:				Dict						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				str						=    outputs.loss
																UpperCamelCase_:				List[str]						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin    )    )    )
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used    )    )
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked    )    )
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
								UpperCamelCase_:				Optional[Any]						=    tracemalloc.peaked + bamb(tracemalloc.begin    )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir  ,     """peak_memory_utilization.json"""    )  ,     """w"""    ) as f:
												json.dump(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     default="""bert-base-cased"""  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--output_dir"""  ,     type=lowerCamelCase  ,     default="""."""  ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,     )
				parser.add_argument(
				    """--peak_memory_upper_bound"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""  ,     )
				parser.add_argument(
				    """--n_train"""  ,     type=lowerCamelCase  ,     default=3_20  ,     help="""Number of training examples to use."""  ,     )
				parser.add_argument(
				    """--n_val"""  ,     type=lowerCamelCase  ,     default=1_60  ,     help="""Number of validation examples to use."""  ,     )
				parser.add_argument(
				    """--num_epochs"""  ,     type=lowerCamelCase  ,     default=1  ,     help="""Number of train epochs."""  ,     )
				UpperCamelCase_:				Optional[int]						=    parser.parse_args()
				UpperCamelCase_:				Union[str, Any]						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()






